scholarly journals Three-dimensional and non-destructive characterization of nerves inside conduits using laboratory-based micro computed tomography

2018 ◽  
Vol 294 ◽  
pp. 59-66 ◽  
Author(s):  
Christos Bikis ◽  
Peter Thalmann ◽  
Lucas Degrugillier ◽  
Georg Schulz ◽  
Bert Müller ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Three Dimensional ◽  
Micro Computed Tomography ◽  
Non Destructive ◽  
Non Destructive Characterization

scholarly journals 3D GEOMETRIC CHARACTERIZATION OF PARTICLES APPLIED TO TECHNICAL CLEANLINESS

2012 ◽  
Vol 31 (3) ◽  
pp. 163 ◽  
Author(s):  
Irene Vecchio ◽  
Katja Schladitz ◽  
Michael Godehardt ◽  
Markus J. Heneka
Keyword(s):  
Computed Tomography ◽  
Three Dimensional ◽  
Geometric Features ◽  
Spatial Structures ◽  
Micro Computed Tomography ◽  
New Methods ◽  
Reliable Classification ◽  
2D Data ◽  
2D Images

During production of mechanical components, residual dirt collects on the surfaces, thus creating a contamination that affects the durability of the assembled products. Residual particles are currently analyzed based on microscopic 2d images. However, the particle's shape is decisive for the damage it can cause, yet can not be judged reliably from 2d data. Micro-computed tomography allows to capture the complex spatial structures of thousands of particles simultaneously. Now new methods to characterize three dimensional shapes are needed to establish 3d cleanliness analysis. In this work, unambiguously indicative geometric features are defined and it is investigated how they can yield a reliable classification in three typical classes: fibers, chips and granules. Finally, the efficiency of the proposed method is proved by analyzing samples of real dirt particles.

Non-destructive characterization of coal samples from China using microfocus X-ray computed tomography

2009 ◽  
Vol 80 (2) ◽  
pp. 113-123 ◽  
Author(s):  
Yanbin Yao ◽  
Dameng Liu ◽  
Yao Che ◽  
Dazhen Tang ◽  
Shuheng Tang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
X Ray ◽  
X Ray Computed ◽  
Non Destructive ◽  
Non Destructive Characterization

Modeling Dental Implants With Finite Element Analysis and Micro-Computed Tomography

2009 ◽  
Author(s):  
Naomi Tsafnat
Keyword(s):  
Computed Tomography ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Three Dimensional ◽  
Structural Response ◽  
Micro Computed Tomography ◽  
Element Analysis ◽  
Numerical Modeling And Simulation ◽  
Non Destructive ◽  
Digital Nature

X-ray micro-computed tomography (microCT) allows us to construct three-dimensional images of specimens at the micron scale in a non-destructive manner. The digital nature of the microCT images, which are in voxel form, make them ideal candidates for use in numerical modeling and simulation [1]. Finite element analysis (FEA) is a well-known technique for modeling the structural response of a system to mechanical loading, and is most useful in modeling complex systems which cannot be analyzed analytically. MicroCT datasets can be converted into finite element models, directly incorporating both the geometry of the specimen and information about the different materials in it. This method is known as micro-finite element analysis (microFEA). It is especially useful in the study of materials with complex microstructures.

scholarly journals Three-dimensional Non-destructive Visualization of Teeth Enamel Microcracks Using X-ray Micro-computed Tomography

2021 ◽  
Author(s):  
Irma Dumbryte ◽  
Arturas Vailionis ◽  
Edvinas Skliutas ◽  
Saulius Juodkazis ◽  
Mangirdas Malinauskas
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
3D Visualization ◽  
Three Dimensional ◽  
Potential Effect ◽  
Micro Computed Tomography ◽  
X Ray ◽  
Tooth Structure ◽  
Structure Damage ◽  
Non Destructive

Abstract Although the topic of tooth fractures has been extensively analyzed in the dental literature, there is still insufficient information on the potential effect of enamel microcracks (EMCs) to the underlying tooth structures. For precise examination of tooth structure damage in the area of EMCs (i.e. whether it crosses the dentin-enamel junction (DEJ) and reaches dentin or pulp), volumetric (three-dimensional (3D)) evaluation of EMCs is necessary. The aim of this study was to present an X-ray micro-computed tomography (μCT) as a technique suitable for 3D non-destructive visualization and qualitative analysis of different severity teeth EMCs. Extracted human maxillary premolars were examined using a μCT instrument ZEISS Xradia 520 Versa. In order to separate (segment) cracks from the rest of the tooth a Deep Learning Tool was utilized within the ORS Dragonfly software. The scanning technique used allowed for the recognition and detection of EMCs that are not only visible on the outer surface but also those that are deeply buried inside the tooth. The 3D visualization combined with Deep Learning segmentation enabled evaluation of EMC dynamics as it extends from the cervical to the occlusal part of the tooth, and precise examination of EMC position with respect to the DEJ.

scholarly journals Three-dimensional non-destructive visualization of teeth enamel microcracks using X-ray micro-computed tomography

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Irma Dumbryte ◽  
Arturas Vailionis ◽  
Edvinas Skliutas ◽  
Saulius Juodkazis ◽  
Mangirdas Malinauskas
Keyword(s):  
Computed Tomography ◽  
3D Visualization ◽  
Three Dimensional ◽  
Potential Effect ◽  
Micro Computed Tomography ◽  
X Ray ◽  
Tooth Structure ◽  
Maxillary Premolars ◽  
Non Destructive ◽  
3D Evaluation

AbstractAlthough the topic of tooth fractures has been extensively analyzed in the dental literature, there is still insufficient information about the potential effect of enamel microcracks (EMCs) on the underlying tooth structures. For a precise examination of the extent of the damage to the tooth structure in the area of EMCs, it is necessary to carry out their volumetric [(three-dimensional (3D)] evaluation. The aim of this study was to validate an X-ray micro-computed tomography ($$\mu $$ μ CT) as a technique suitable for 3D non-destructive visualization and qualitative analysis of teeth EMCs of different severity. Extracted human maxillary premolars were examined using a $$\mu $$ μ CT instrument ZEISS Xradia 520 Versa. In order to separate crack, dentin, and enamel volumes a Deep Learning (DL) algorithm, part of the Dragonfly’s segmentation toolkit, was utilized. For segmentation needs we implemented Dragonfly’s pre-built UNet neural network. The scanning technique which was used made it possible to recognize and detect not only EMCs that are visible on the outer surface but also those that are buried deep inside the tooth. The 3D visualization, combined with DL assisted segmentation, enabled the evaluation of the dynamics of an EMC and precise examination of its position with respect to the dentin-enamel junction.

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